Trajectories in smoking habits and outcomes in patients with stable coronary artery disease
Bibliographic record
Abstract
Abstract Background Smoking is a well-identified risk factor for recurrent cardiovascular (CV) events in patients with stable coronary artery disease (CAD). However, smoking status is not binary, and there is a wide diversity of smoking habits trajectories within this population, with different consequences on residual CV risk. Purpose To better understand how smoking status evolves in patients with CAD, and how it is associated with subsequent CV events. Methods We analysed the CLARIFY registry, which included 32,378 patients with CAD. We defined groups according to smoking status at inclusion in the registry: never, former and active smokers. For former smokers, we assessed the timing of definitive smoking discontinuation and its relationship with subsequent CV events. For active smokers, we investigated the association between smoking reduction or discontinuation in active smokers with outcomes. Our primary endpoint was the occurrence of MACE defined by CV death or myocardial infarction (MI) during the 5-year follow-up period adjusted on known predictors for MACE in CAD patients. Results At inclusion, 13,366 (41.3%) patients had never smoked, 14,973 (46.2%) were former smokers, and 4,039 (12.5%) were current smokers, after an average of 6.5 (±6.3) years following CAD diagnosis. In former smokers that were smoking at the time of CAD diagnosis, 72.8% definitively discontinued smoking within the first year following diagnosis, while only 27.2% quit in subsequent years. Each additional year of active smoking after CAD diagnosis increased the risk of MACE (adjusted HR [aHR] 1.08, 95% CI 1.04-1.12 per year). However, regardless of the duration of smoking cessation, former smokers did not return to the cardiovascular risk levels observed in individuals who never smoked (figure). In active smokers at inclusion, smoking cessation was associated with improved CV outcomes, irrespective of the timing of cessation during follow-up (aHR 0.56, 95% CI 0.42-0.76, p < 0.001). Among active smokers, 25.7% reduced their quantity smoked within the first year of follow-up without quitting, but this did not significantly reduce the rate of CV death and MI (aHR 0.96, 95% CI 0.74-1.26, p = 0.78) compared to other active smokers. Conclusion In active smokers diagnosed with CAD, the vast majority of definite quitters cease smoking within the first year of CAD diagnosis. Each subsequent year of active smoking adversely affects CV outcomes. Smoking cessation at any time significantly reduces the risk of CV death or recurrent MI compared to persistent active smokers, although CV risk of former smokers does not decrease to the levels observed in never-smokers. Finally, smoking quantity reduction is not enough to reduce CV risk in this population. These findings underscore the critical importance of timely interventions for smokers diagnosed with CAD, and emphasize that when it comes to smoking cessation, sooner is better, but it is never too late.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".